動態噪聲特性未知系統的多模型自適應卡爾曼濾波

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基金項目:

國家自然科學基金資助項目(No.60604002)

北京市教委共建重點學科資助項目(No.xk100080537).

北京市科技新星計劃資助項目(No.2006B23)

北京科技大學422高層次引進人才計劃資助項目

Multiple model adaptive Kalman filter for the system without the knowledge of process noise

1. School of Information Engineering, University of Science and Technology Beijing, Beijing 100083, China;
2. College of Information Science and Engineering, Northeastern University, Shenyang 110004, China

摘要: 針對常規自適應卡爾曼濾波器存在過渡過程差的問題,基于一個給定的指標切換函數,采用多個基于不同動態噪聲協方差矩陣的卡爾曼濾波器和一個常規自適應卡爾曼濾波器共同組成多模型自適應卡爾曼濾波器.與常規自適應卡爾曼濾波器相比,多模型自適應卡爾曼濾波器可以在保持原有自適應濾波器性能的基礎上極大地改善瞬態響應.
- 噪聲特性 /
- 未知系統 /
- 自適應卡爾曼濾波 /
- 切換 /
- 多模型
Abstract: To solve the bad transient response of a conventional adaptive Kalman filter, multiple Kalman filters based on different noise covariances and a conventional adaptive Kalman filter (AKF) were used to form a multiple model adaptive Kalman filter (MMAKF) by using a switching index function. Compared with a conventional AKF, the MMAKF could improve the transient response greatly without losing the characteristic of the conventional AKF.
- noise characteristic /
- unknown system /
- adaptive Kalman filtering /
- switching /
- multiple model